{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dUeKVCYTbcyT"
   },
   "source": [
    "#  Advanced Logistic Regression in TensorFlow 2.0 \n",
    "\n",
    "\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "1. Load a CSV file using Pandas\n",
    "2. Create train, validation, and test sets\n",
    "3. Define and train a model using Keras (including setting class weights)\n",
    "4. Evaluate the model using various metrics (including precision and recall)\n",
    "5. Try common techniques for dealing with imbalanced data:\n",
    "    Class weighting and\n",
    "    Oversampling\n",
    "\n",
    "\n",
    "\n",
    "## Introduction \n",
    "This lab how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use [Keras](../../guide/keras/overview.ipynb) to define the model and [class weights](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model) to help the model learn from the imbalanced data. \n",
    "\n",
    "PENDING LINK UPDATE:  Each learning objective will correspond to a __#TODO__ in the [student lab notebook](https://training-data-analyst/courses/machine_learning/deepdive2/image_classification/labs/5_fashion_mnist_class.ipynb) -- try to complete that notebook first before reviewing this solution notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kRHmSyHxEIhN"
   },
   "source": [
    "Start by importing the necessary libraries for this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JM7hDSNClfoK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version:  2.5.0\n"
     ]
    }
   ],
   "source": [
    "# You can use any Python source file as a module by executing an import statement in some other Python source file.\n",
    "# The import statement combines two operations; it searches for the named module, then it binds the\n",
    "# results of that search to a name in the local scope.\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "# Use matplotlib for visualizing the model\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "# Here we'll import Pandas and Numpy data processing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# Use seaborn for data visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning.\n",
    "import sklearn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "print(\"TensorFlow version: \",tf.version.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cell, we're going to customize our Matplot lib visualization figure size and colors. Note that each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element we create. This configuration can be adjusted at any time using the plt.rc convenience routine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c8o1FHzD-_y_"
   },
   "outputs": [],
   "source": [
    "# Customize our Matplot lib visualization figure size and colors\n",
    "mpl.rcParams['figure.figsize'] = (12, 10)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z3iZVjziKHmX"
   },
   "source": [
    "## Data processing and exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4sA9WOcmzH2D"
   },
   "source": [
    "### Download the Kaggle Credit Card Fraud data set\n",
    "\n",
    "Pandas is a Python library with many helpful utilities for loading and working with structured data and can be used to download CSVs into a dataframe.\n",
    "\n",
    "Note: This dataset has been collected and analysed during a research collaboration of Worldline and the [Machine Learning Group](http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available [here](https://www.researchgate.net/project/Fraud-detection-5) and the page of the [DefeatFraud](https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/) project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pR_SnbMArXr7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = tf.keras.utils\n",
    "# pandas module read_csv() function reads the CSV file into a DataFrame object.\n",
    "raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n",
    "# `head()` function is used to get the first n rows of dataframe\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's view the statistics of the raw dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-fgdQgmwUFuj"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   9.604066e-16  1.687098e-15 -3.666453e-16 -1.220404e-16      88.349619   \n",
       "std    1.380247e+00  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.137433e+02 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -6.915971e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%   -5.433583e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    6.119264e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    3.480167e+01  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() is used to view some basic statistical details\n",
    "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xWKB_CVZFLpB"
   },
   "source": [
    "### Examine the class label imbalance\n",
    "\n",
    "Let's look at the dataset imbalance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HCJFrtuY2iLF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples:\n",
      "    Total: 284807\n",
      "    Positive: 492 (0.17% of total)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Numpy bincount() method is used to obtain the frequency of each element provided inside a numpy array\n",
    "neg, pos = np.bincount(raw_df['Class'])\n",
    "total = neg + pos\n",
    "print('Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n'.format(\n",
    "    total, pos, 100 * pos / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KnLKFQDsCBUg"
   },
   "source": [
    "This shows the small fraction of positive samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6qox6ryyzwdr"
   },
   "source": [
    "### Clean, split and normalize the data\n",
    "\n",
    "The raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ef42jTuxEjnj"
   },
   "outputs": [],
   "source": [
    "cleaned_df = raw_df.copy()\n",
    "\n",
    "# You don't want the `Time` column.\n",
    "cleaned_df.pop('Time')\n",
    "\n",
    "# The `Amount` column covers a huge range. Convert to log-space.\n",
    "eps=0.001 # 0 => 0.1¢\n",
    "cleaned_df['Log Ammount'] = np.log(cleaned_df.pop('Amount')+eps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uSNgdQFFFQ6u"
   },
   "source": [
    "Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where [overfitting](https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting) is a significant concern from the lack of training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xfxhKg7Yr1-b"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "# Use a utility from sklearn to split and shuffle our dataset.\n",
    "# train_test_split() method split arrays or matrices into random train and test subsets\n",
    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.2)\n",
    "\n",
    "# Form np arrays of labels and features.\n",
    "train_labels = np.array(train_df.pop('Class'))\n",
    "bool_train_labels = train_labels != 0\n",
    "val_labels = np.array(val_df.pop('Class'))\n",
    "test_labels = np.array(test_df.pop('Class'))\n",
    "\n",
    "train_features = np.array(train_df)\n",
    "val_features = np.array(val_df)\n",
    "test_features = np.array(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8a_Z_kBmr7Oh"
   },
   "source": [
    "Normalize the input features using the sklearn StandardScaler.\n",
    "This will set the mean to 0 and standard deviation to 1.\n",
    "\n",
    "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IO-qEUmJ5JQg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training labels shape: (182276,)\n",
      "Validation labels shape: (45569,)\n",
      "Test labels shape: (56962,)\n",
      "Training features shape: (182276, 29)\n",
      "Validation features shape: (45569, 29)\n",
      "Test features shape: (56962, 29)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "train_features = scaler.fit_transform(train_features)\n",
    "\n",
    "val_features = scaler.transform(val_features)\n",
    "test_features = scaler.transform(test_features)\n",
    "\n",
    "# `np.clip()` clip (limit) the values in an array.\n",
    "train_features = np.clip(train_features, -5, 5)\n",
    "val_features = np.clip(val_features, -5, 5)\n",
    "test_features = np.clip(test_features, -5, 5)\n",
    "\n",
    "\n",
    "print('Training labels shape:', train_labels.shape)\n",
    "print('Validation labels shape:', val_labels.shape)\n",
    "print('Test labels shape:', test_labels.shape)\n",
    "\n",
    "print('Training features shape:', train_features.shape)\n",
    "print('Validation features shape:', val_features.shape)\n",
    "print('Test features shape:', test_features.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XF2nNfWKJ33w"
   },
   "source": [
    "Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uQ7m9nqDC3W6"
   },
   "source": [
    "### Look at the data distribution\n",
    "\n",
    "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n",
    "\n",
    "* Do these distributions make sense? \n",
    "    * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n",
    "* Can you see the difference between the ditributions?\n",
    "    * Yes the positive examples contain a much higher rate of extreme values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "raK7hyjd_vf6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns)\n",
    "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns = train_df.columns)\n",
    "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns = train_df.columns)\n",
    "\n",
    "# Seaborn’s jointplot displays a relationship between 2 variables (bivariate) as well as\n",
    "sns.jointplot(pos_df['V5'], pos_df['V6'],\n",
    "              kind='hex', xlim = (-5,5), ylim = (-5,5))\n",
    "# The suptitle() function in pyplot module of the matplotlib library is used to add a title to the figure.\n",
    "plt.suptitle(\"Positive distribution\")\n",
    "\n",
    "sns.jointplot(neg_df['V5'], neg_df['V6'],\n",
    "              kind='hex', xlim = (-5,5), ylim = (-5,5))\n",
    "_ = plt.suptitle(\"Negative distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qFK1u4JX16D8"
   },
   "source": [
    "## Define the model and metrics\n",
    "\n",
    "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3JQDzUqT3UYG"
   },
   "outputs": [],
   "source": [
    "METRICS = [\n",
    "      keras.metrics.TruePositives(name='tp'),\n",
    "      keras.metrics.FalsePositives(name='fp'),\n",
    "      keras.metrics.TrueNegatives(name='tn'),\n",
    "      keras.metrics.FalseNegatives(name='fn'), \n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "      keras.metrics.Precision(name='precision'),\n",
    "      keras.metrics.Recall(name='recall'),\n",
    "      keras.metrics.AUC(name='auc'),\n",
    "]\n",
    "\n",
    "def make_model(metrics = METRICS, output_bias=None):\n",
    "  if output_bias is not None:\n",
    "# `tf.keras.initializers.Constant()` generates tensors with constant values.\n",
    "    output_bias = tf.keras.initializers.Constant(output_bias)\n",
    "# TODO 1\n",
    "# Creating a Sequential model\n",
    "  model = keras.Sequential([\n",
    "      keras.layers.Dense(\n",
    "          16, activation='relu',\n",
    "          input_shape=(train_features.shape[-1],)),\n",
    "      keras.layers.Dropout(0.5),\n",
    "      keras.layers.Dense(1, activation='sigmoid',\n",
    "                         bias_initializer=output_bias),\n",
    "  ])\n",
    "\n",
    "# Compile the model\n",
    "  model.compile(\n",
    "      optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "      loss=keras.losses.BinaryCrossentropy(),\n",
    "      metrics=metrics)\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SU0GX6E6mieP"
   },
   "source": [
    "### Understanding useful metrics\n",
    "\n",
    "Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.\n",
    "\n",
    "\n",
    "\n",
    "*   **False** negatives and **false** positives are samples that were **incorrectly** classified\n",
    "*   **True** negatives and **true** positives are samples that were **correctly** classified\n",
    "*   **Accuracy** is the percentage of examples correctly classified\n",
    ">   $\\frac{\\text{true samples}}{\\text{total samples}}$\n",
    "*   **Precision** is the percentage of **predicted** positives that were correctly classified\n",
    ">   $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$\n",
    "*   **Recall** is the percentage of **actual** positives that were correctly classified\n",
    ">   $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$\n",
    "*   **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than than a random negative sample.\n",
    "\n",
    "Note: Accuracy is not a helpful metric for this task. You can 99.8%+ accuracy on this task by predicting False all the time.  \n",
    "\n",
    "Read more:\n",
    "*  [True vs. False and Positive vs. Negative](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)\n",
    "*  [Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy)\n",
    "*   [Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n",
    "*   [ROC-AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FYdhSAoaF_TK"
   },
   "source": [
    "## Baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IDbltVPg2m2q"
   },
   "source": [
    "### Build the model\n",
    "\n",
    "Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.\n",
    "\n",
    "\n",
    "Note: this model will not handle the class imbalance well. You will improve it later in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ouUkwPcGQsy3"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 100\n",
    "BATCH_SIZE = 2048\n",
    "\n",
    "# Stop training when a monitored metric has stopped improving.\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_auc', \n",
    "    verbose=1,\n",
    "    patience=10,\n",
    "    mode='max',\n",
    "    restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1xlR_dekzw7C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_16 (Dense)             (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 497\n",
      "Trainable params: 497\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Display a model summary\n",
    "model = make_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wx7ND3_SqckO"
   },
   "source": [
    "Test run the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LopSd-yQqO3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89924395],\n",
       "       [0.7323974 ],\n",
       "       [0.9322966 ],\n",
       "       [0.8881701 ],\n",
       "       [0.88115484],\n",
       "       [0.6485833 ],\n",
       "       [0.79132897],\n",
       "       [0.7073316 ],\n",
       "       [0.8343261 ],\n",
       "       [0.8008822 ]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the model to do prediction with model.predict()\n",
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YKIgWqHms_03"
   },
   "source": [
    "### Optional: Set the correct initial bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qk_3Ry6EoYDq"
   },
   "source": [
    "These are initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines)). This can help with initial convergence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PdbfWDuVpo6k"
   },
   "source": [
    "With the default bias initialization the loss should be about `math.log(2) = 0.69314` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H-oPqh3SoGXk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 1.7441\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
    "print(\"Loss: {:0.4f}\".format(results[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hE-JRzfKqfhB"
   },
   "source": [
    "The correct bias to set can be derived from:\n",
    "\n",
    "$$ p_0 = pos/(pos + neg) = 1/(1+e^{-b_0}) $$\n",
    "$$ b_0 = -log_e(1/p_0 - 1) $$\n",
    "$$ b_0 = log_e(pos/neg)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F5KWPSjjstUS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.35935934])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.log() is a mathematical function that is used to calculate the natural logarithm.\n",
    "initial_bias = np.log([pos/neg])\n",
    "initial_bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d1juXI9yY1KD"
   },
   "source": [
    "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n",
    "\n",
    "It should be near: `pos/total = 0.0018`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "50oyu1uss0i-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00196099],\n",
       "       [0.00737071],\n",
       "       [0.00182639],\n",
       "       [0.00342294],\n",
       "       [0.00442886],\n",
       "       [0.00714428],\n",
       "       [0.0061818 ],\n",
       "       [0.00631511],\n",
       "       [0.0088356 ],\n",
       "       [0.01214694]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = make_model(output_bias = initial_bias)\n",
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4xqFYb2KqRHQ"
   },
   "source": [
    "With this initialization the initial loss should be approximately:\n",
    "\n",
    "$$-p_0log(p_0)-(1-p_0)log(1-p_0) = 0.01317$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xVDqCWXDqHSc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0275\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
    "print(\"Loss: {:0.4f}\".format(results[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FrDC8hvNr9yw"
   },
   "source": [
    "This initial loss is about 50 times less than if would have been with naive initilization.\n",
    "\n",
    "This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. This also makes it easier to read plots of the loss during training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0EJj9ixKVBMT"
   },
   "source": [
    "### Checkpoint the initial weights\n",
    "\n",
    "To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_tSUm4yAVIif"
   },
   "outputs": [],
   "source": [
    "initial_weights = os.path.join(tempfile.mkdtemp(),'initial_weights')\n",
    "model.save_weights(initial_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EVXiLyqyZ8AX"
   },
   "source": [
    "### Confirm that the bias fix helps\n",
    "\n",
    "Before moving on, confirm quick that the careful bias initialization actually helped.\n",
    "\n",
    "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dm4-4K5RZ63Q"
   },
   "outputs": [],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "model.layers[-1].bias.assign([0.0])\n",
    "# Fit data to model\n",
    "zero_bias_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels), \n",
    "    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j8DsLXHQaSql"
   },
   "outputs": [],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "careful_bias_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels), \n",
    "    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E3XsMBjhauFV"
   },
   "outputs": [],
   "source": [
    "def plot_loss(history, label, n):\n",
    "  # Use a log scale to show the wide range of values.\n",
    "  plt.semilogy(history.epoch,  history.history['loss'],\n",
    "               color=colors[n], label='Train '+label)\n",
    "  plt.semilogy(history.epoch,  history.history['val_loss'],\n",
    "          color=colors[n], label='Val '+label,\n",
    "          linestyle=\"--\")\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')\n",
    "  \n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dxFaskm7beC7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n",
    "plot_loss(careful_bias_history, \"Careful Bias\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fKMioV0ddG3R"
   },
   "source": [
    "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RsA_7SEntRaV"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yZKAc8NCDnoR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 182276 samples, validate on 45569 samples\n",
      "Epoch 1/100\n",
      "182276/182276 [==============================] - 3s 16us/sample - loss: 0.0256 - tp: 64.0000 - fp: 745.0000 - tn: 181227.0000 - fn: 240.0000 - accuracy: 0.9946 - precision: 0.0791 - recall: 0.2105 - auc: 0.8031 - val_loss: 0.0079 - val_tp: 17.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 66.0000 - val_accuracy: 0.9984 - val_precision: 0.7083 - val_recall: 0.2048 - val_auc: 0.9377\n",
      "Epoch 2/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0100 - tp: 111.0000 - fp: 131.0000 - tn: 181841.0000 - fn: 193.0000 - accuracy: 0.9982 - precision: 0.4587 - recall: 0.3651 - auc: 0.8758 - val_loss: 0.0056 - val_tp: 40.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 43.0000 - val_accuracy: 0.9989 - val_precision: 0.8511 - val_recall: 0.4819 - val_auc: 0.9422\n",
      "Epoch 3/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0075 - tp: 148.0000 - fp: 57.0000 - tn: 181915.0000 - fn: 156.0000 - accuracy: 0.9988 - precision: 0.7220 - recall: 0.4868 - auc: 0.9206 - val_loss: 0.0048 - val_tp: 52.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 31.0000 - val_accuracy: 0.9992 - val_precision: 0.8814 - val_recall: 0.6265 - val_auc: 0.9382\n",
      "Epoch 4/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0065 - tp: 157.0000 - fp: 48.0000 - tn: 181924.0000 - fn: 147.0000 - accuracy: 0.9989 - precision: 0.7659 - recall: 0.5164 - auc: 0.9210 - val_loss: 0.0045 - val_tp: 52.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 31.0000 - val_accuracy: 0.9992 - val_precision: 0.8814 - val_recall: 0.6265 - val_auc: 0.9387\n",
      "Epoch 5/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0058 - tp: 172.0000 - fp: 43.0000 - tn: 181929.0000 - fn: 132.0000 - accuracy: 0.9990 - precision: 0.8000 - recall: 0.5658 - auc: 0.9246 - val_loss: 0.0042 - val_tp: 51.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 32.0000 - val_accuracy: 0.9991 - val_precision: 0.8793 - val_recall: 0.6145 - val_auc: 0.9390\n",
      "Epoch 6/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0054 - tp: 169.0000 - fp: 28.0000 - tn: 181944.0000 - fn: 135.0000 - accuracy: 0.9991 - precision: 0.8579 - recall: 0.5559 - auc: 0.9210 - val_loss: 0.0039 - val_tp: 56.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 27.0000 - val_accuracy: 0.9993 - val_precision: 0.8889 - val_recall: 0.6747 - val_auc: 0.9391\n",
      "Epoch 7/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0054 - tp: 167.0000 - fp: 33.0000 - tn: 181939.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8350 - recall: 0.5493 - auc: 0.9224 - val_loss: 0.0038 - val_tp: 60.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 23.0000 - val_accuracy: 0.9993 - val_precision: 0.8955 - val_recall: 0.7229 - val_auc: 0.9392\n",
      "Epoch 8/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0050 - tp: 182.0000 - fp: 28.0000 - tn: 181944.0000 - fn: 122.0000 - accuracy: 0.9992 - precision: 0.8667 - recall: 0.5987 - auc: 0.9215 - val_loss: 0.0038 - val_tp: 62.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_precision: 0.8986 - val_recall: 0.7470 - val_auc: 0.9332\n",
      "Epoch 9/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0047 - tp: 186.0000 - fp: 36.0000 - tn: 181936.0000 - fn: 118.0000 - accuracy: 0.9992 - precision: 0.8378 - recall: 0.6118 - auc: 0.9238 - val_loss: 0.0036 - val_tp: 63.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9000 - val_recall: 0.7590 - val_auc: 0.9332\n",
      "Epoch 10/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0048 - tp: 176.0000 - fp: 33.0000 - tn: 181939.0000 - fn: 128.0000 - accuracy: 0.9991 - precision: 0.8421 - recall: 0.5789 - auc: 0.9208 - val_loss: 0.0036 - val_tp: 63.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9000 - val_recall: 0.7590 - val_auc: 0.9332\n",
      "Epoch 11/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0045 - tp: 180.0000 - fp: 32.0000 - tn: 181940.0000 - fn: 124.0000 - accuracy: 0.9991 - precision: 0.8491 - recall: 0.5921 - auc: 0.9341 - val_loss: 0.0035 - val_tp: 64.0000 - val_fp: 7.0000 - val_tn: 45479.0000 - val_fn: 19.0000 - val_accuracy: 0.9994 - val_precision: 0.9014 - val_recall: 0.7711 - val_auc: 0.9331\n",
      "Epoch 12/100\n",
      "169984/182276 [==========================>...] - ETA: 0s - loss: 0.0045 - tp: 175.0000 - fp: 30.0000 - tn: 169674.0000 - fn: 105.0000 - accuracy: 0.9992 - precision: 0.8537 - recall: 0.6250 - auc: 0.9306Restoring model weights from the end of the best epoch.\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.0045 - tp: 188.0000 - fp: 31.0000 - tn: 181941.0000 - fn: 116.0000 - accuracy: 0.9992 - precision: 0.8584 - recall: 0.6184 - auc: 0.9326 - val_loss: 0.0034 - val_tp: 63.0000 - val_fp: 6.0000 - val_tn: 45480.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.9130 - val_recall: 0.7590 - val_auc: 0.9332\n",
      "Epoch 00012: early stopping\n"
     ]
    }
   ],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "# Fit data to model\n",
    "baseline_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks = [early_stopping],\n",
    "    validation_data=(val_features, val_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iSaDBYU9xtP6"
   },
   "source": [
    "### Check training history\n",
    "In this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in this [tutorial](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit).\n",
    "\n",
    "Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WTSkhT1jyGu6"
   },
   "outputs": [],
   "source": [
    "def plot_metrics(history):\n",
    "  metrics =  ['loss', 'auc', 'precision', 'recall']\n",
    "  for n, metric in enumerate(metrics):\n",
    "    name = metric.replace(\"_\",\" \").capitalize()\n",
    "# subplots() which acts as a utility wrapper and helps in creating common layouts of subplots\n",
    "    plt.subplot(2,2,n+1)\n",
    "    plt.plot(history.epoch,  history.history[metric], color=colors[0], label='Train')\n",
    "    plt.plot(history.epoch, history.history['val_'+metric],\n",
    "             color=colors[0], linestyle=\"--\", label='Val')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel(name)\n",
    "    if metric == 'loss':\n",
    "      plt.ylim([0, plt.ylim()[1]])\n",
    "    elif metric == 'auc':\n",
    "      plt.ylim([0.8,1])\n",
    "    else:\n",
    "      plt.ylim([0,1])\n",
    "\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u6LReDsqlZlk"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UCa4iWo6WDKR"
   },
   "source": [
    "Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aJC1booryouo"
   },
   "source": [
    "### Evaluate metrics\n",
    "\n",
    "You can use a [confusion matrix](https://developers.google.com/machine-learning/glossary/#confusion_matrix) to summarize the actual vs. predicted labels where the X axis is the predicted label and the Y axis is the actual label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aNS796IJKrev"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MVWBGfADwbWI"
   },
   "outputs": [],
   "source": [
    "def plot_cm(labels, predictions, p=0.5):\n",
    "  cm = confusion_matrix(labels, predictions > p)\n",
    "  plt.figure(figsize=(5,5))\n",
    "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "  plt.title('Confusion matrix @{:.2f}'.format(p))\n",
    "  plt.ylabel('Actual label')\n",
    "  plt.xlabel('Predicted label')\n",
    "\n",
    "  print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n",
    "  print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n",
    "  print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n",
    "  print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n",
    "  print('Total Fraudulent Transactions: ', np.sum(cm[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nOTjD5Z5Wp1U"
   },
   "source": [
    "Evaluate your model on the test dataset and display the results for the metrics you created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "poh_hZngt2_9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.005941324691873794\n",
      "tp :  55.0\n",
      "fp :  12.0\n",
      "tn :  56845.0\n",
      "fn :  50.0\n",
      "accuracy :  0.99891156\n",
      "precision :  0.8208955\n",
      "recall :  0.52380955\n",
      "auc :  0.9390888\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  56845\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  12\n",
      "Fraudulent Transactions Missed (False Negatives):  50\n",
      "Fraudulent Transactions Detected (True Positives):  55\n",
      "Total Fraudulent Transactions:  105\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "baseline_results = model.evaluate(test_features, test_labels,\n",
    "                                  batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PyZtSr1v6L4t"
   },
   "source": [
    "If the model had predicted everything perfectly, this would be a [diagonal matrix](https://en.wikipedia.org/wiki/Diagonal_matrix) where values off the main diagonal, indicating incorrect predictions, would be zero. In this case the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged. However, you would likely want to have even fewer false negatives despite the cost of increasing the number of false positives. This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P-QpQsip_F2Q"
   },
   "source": [
    "### Plot the ROC\n",
    "\n",
    "Now plot the [ROC](https://developers.google.com/machine-learning/glossary#ROC). This plot is useful because it shows, at a glance, the range of performance the model can reach just by tuning the output threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lhaxsLSvANF9"
   },
   "outputs": [],
   "source": [
    "def plot_roc(name, labels, predictions, **kwargs):\n",
    "# Plot Receiver operating characteristic (ROC) curve.\n",
    "  fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
    "\n",
    "  plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)\n",
    "  plt.xlabel('False positives [%]')\n",
    "  plt.ylabel('True positives [%]')\n",
    "  plt.xlim([-0.5,20])\n",
    "  plt.ylim([80,100.5])\n",
    "  plt.grid(True)\n",
    "  ax = plt.gca()\n",
    "  ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DfHHspttKJE0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5f28134cf8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gpdsFyp64DhY"
   },
   "source": [
    "It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cveQoiMyGQCo"
   },
   "source": [
    "## Class weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ePGp6GUE1WfH"
   },
   "source": [
    "### Calculate class weights\n",
    "\n",
    "The goal is to identify fradulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qjGWErngGny7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight for class 0: 0.50\n",
      "Weight for class 1: 289.44\n"
     ]
    }
   ],
   "source": [
    "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
    "# The sum of the weights of all examples stays the same.\n",
    "# TODO 1\n",
    "weight_for_0 = (1 / neg)*(total)/2.0 \n",
    "weight_for_1 = (1 / pos)*(total)/2.0\n",
    "\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
    "\n",
    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
    "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mk1OOE2ZSHzy"
   },
   "source": [
    "### Train a model with class weights\n",
    "\n",
    "Now try re-training and evaluating the model with class weights to see how that affects the predictions.\n",
    "\n",
    "Note: Using `class_weights` changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like `optimizers.SGD`, may fail. The optimizer used here, `optimizers.Adam`, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UJ589fn8ST3x"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train on 182276 samples, validate on 45569 samples\n",
      "Epoch 1/100\n",
      "182276/182276 [==============================] - 3s 19us/sample - loss: 1.0524 - tp: 138.0000 - fp: 2726.0000 - tn: 179246.0000 - fn: 166.0000 - accuracy: 0.9841 - precision: 0.0482 - recall: 0.4539 - auc: 0.8321 - val_loss: 0.4515 - val_tp: 59.0000 - val_fp: 432.0000 - val_tn: 45054.0000 - val_fn: 24.0000 - val_accuracy: 0.9900 - val_precision: 0.1202 - val_recall: 0.7108 - val_auc: 0.9492\n",
      "Epoch 2/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.5537 - tp: 216.0000 - fp: 3783.0000 - tn: 178189.0000 - fn: 88.0000 - accuracy: 0.9788 - precision: 0.0540 - recall: 0.7105 - auc: 0.9033 - val_loss: 0.3285 - val_tp: 69.0000 - val_fp: 514.0000 - val_tn: 44972.0000 - val_fn: 14.0000 - val_accuracy: 0.9884 - val_precision: 0.1184 - val_recall: 0.8313 - val_auc: 0.9605\n",
      "Epoch 3/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.4178 - tp: 238.0000 - fp: 4540.0000 - tn: 177432.0000 - fn: 66.0000 - accuracy: 0.9747 - precision: 0.0498 - recall: 0.7829 - auc: 0.9237 - val_loss: 0.2840 - val_tp: 69.0000 - val_fp: 570.0000 - val_tn: 44916.0000 - val_fn: 14.0000 - val_accuracy: 0.9872 - val_precision: 0.1080 - val_recall: 0.8313 - val_auc: 0.9669\n",
      "Epoch 4/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3848 - tp: 247.0000 - fp: 5309.0000 - tn: 176663.0000 - fn: 57.0000 - accuracy: 0.9706 - precision: 0.0445 - recall: 0.8125 - auc: 0.9292 - val_loss: 0.2539 - val_tp: 71.0000 - val_fp: 622.0000 - val_tn: 44864.0000 - val_fn: 12.0000 - val_accuracy: 0.9861 - val_precision: 0.1025 - val_recall: 0.8554 - val_auc: 0.9709\n",
      "Epoch 5/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3596 - tp: 254.0000 - fp: 6018.0000 - tn: 175954.0000 - fn: 50.0000 - accuracy: 0.9667 - precision: 0.0405 - recall: 0.8355 - auc: 0.9323 - val_loss: 0.2363 - val_tp: 72.0000 - val_fp: 713.0000 - val_tn: 44773.0000 - val_fn: 11.0000 - val_accuracy: 0.9841 - val_precision: 0.0917 - val_recall: 0.8675 - val_auc: 0.9725\n",
      "Epoch 6/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3115 - tp: 255.0000 - fp: 6366.0000 - tn: 175606.0000 - fn: 49.0000 - accuracy: 0.9648 - precision: 0.0385 - recall: 0.8388 - auc: 0.9477 - val_loss: 0.2243 - val_tp: 72.0000 - val_fp: 768.0000 - val_tn: 44718.0000 - val_fn: 11.0000 - val_accuracy: 0.9829 - val_precision: 0.0857 - val_recall: 0.8675 - val_auc: 0.9728\n",
      "Epoch 7/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.3179 - tp: 258.0000 - fp: 6804.0000 - tn: 175168.0000 - fn: 46.0000 - accuracy: 0.9624 - precision: 0.0365 - recall: 0.8487 - auc: 0.9435 - val_loss: 0.2165 - val_tp: 72.0000 - val_fp: 812.0000 - val_tn: 44674.0000 - val_fn: 11.0000 - val_accuracy: 0.9819 - val_precision: 0.0814 - val_recall: 0.8675 - val_auc: 0.9739\n",
      "Epoch 8/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2880 - tp: 260.0000 - fp: 6669.0000 - tn: 175303.0000 - fn: 44.0000 - accuracy: 0.9632 - precision: 0.0375 - recall: 0.8553 - auc: 0.9530 - val_loss: 0.2122 - val_tp: 72.0000 - val_fp: 783.0000 - val_tn: 44703.0000 - val_fn: 11.0000 - val_accuracy: 0.9826 - val_precision: 0.0842 - val_recall: 0.8675 - val_auc: 0.9769\n",
      "Epoch 9/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2676 - tp: 262.0000 - fp: 6904.0000 - tn: 175068.0000 - fn: 42.0000 - accuracy: 0.9619 - precision: 0.0366 - recall: 0.8618 - auc: 0.9594 - val_loss: 0.2056 - val_tp: 72.0000 - val_fp: 855.0000 - val_tn: 44631.0000 - val_fn: 11.0000 - val_accuracy: 0.9810 - val_precision: 0.0777 - val_recall: 0.8675 - val_auc: 0.9750\n",
      "Epoch 10/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2498 - tp: 266.0000 - fp: 6833.0000 - tn: 175139.0000 - fn: 38.0000 - accuracy: 0.9623 - precision: 0.0375 - recall: 0.8750 - auc: 0.9593 - val_loss: 0.2001 - val_tp: 73.0000 - val_fp: 840.0000 - val_tn: 44646.0000 - val_fn: 10.0000 - val_accuracy: 0.9813 - val_precision: 0.0800 - val_recall: 0.8795 - val_auc: 0.9761\n",
      "Epoch 11/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2681 - tp: 262.0000 - fp: 6845.0000 - tn: 175127.0000 - fn: 42.0000 - accuracy: 0.9622 - precision: 0.0369 - recall: 0.8618 - auc: 0.9559 - val_loss: 0.1964 - val_tp: 73.0000 - val_fp: 865.0000 - val_tn: 44621.0000 - val_fn: 10.0000 - val_accuracy: 0.9808 - val_precision: 0.0778 - val_recall: 0.8795 - val_auc: 0.9768\n",
      "Epoch 12/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2406 - tp: 268.0000 - fp: 7070.0000 - tn: 174902.0000 - fn: 36.0000 - accuracy: 0.9610 - precision: 0.0365 - recall: 0.8816 - auc: 0.9646 - val_loss: 0.1940 - val_tp: 73.0000 - val_fp: 848.0000 - val_tn: 44638.0000 - val_fn: 10.0000 - val_accuracy: 0.9812 - val_precision: 0.0793 - val_recall: 0.8795 - val_auc: 0.9771\n",
      "Epoch 13/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2285 - tp: 269.0000 - fp: 6976.0000 - tn: 174996.0000 - fn: 35.0000 - accuracy: 0.9615 - precision: 0.0371 - recall: 0.8849 - auc: 0.9680 - val_loss: 0.1930 - val_tp: 73.0000 - val_fp: 857.0000 - val_tn: 44629.0000 - val_fn: 10.0000 - val_accuracy: 0.9810 - val_precision: 0.0785 - val_recall: 0.8795 - val_auc: 0.9772\n",
      "Epoch 14/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2322 - tp: 268.0000 - fp: 6718.0000 - tn: 175254.0000 - fn: 36.0000 - accuracy: 0.9629 - precision: 0.0384 - recall: 0.8816 - auc: 0.9644 - val_loss: 0.1915 - val_tp: 73.0000 - val_fp: 808.0000 - val_tn: 44678.0000 - val_fn: 10.0000 - val_accuracy: 0.9820 - val_precision: 0.0829 - val_recall: 0.8795 - val_auc: 0.9781\n",
      "Epoch 15/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2631 - tp: 267.0000 - fp: 6578.0000 - tn: 175394.0000 - fn: 37.0000 - accuracy: 0.9637 - precision: 0.0390 - recall: 0.8783 - auc: 0.9551 - val_loss: 0.1900 - val_tp: 73.0000 - val_fp: 803.0000 - val_tn: 44683.0000 - val_fn: 10.0000 - val_accuracy: 0.9822 - val_precision: 0.0833 - val_recall: 0.8795 - val_auc: 0.9781\n",
      "Epoch 16/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2314 - tp: 266.0000 - fp: 6644.0000 - tn: 175328.0000 - fn: 38.0000 - accuracy: 0.9633 - precision: 0.0385 - recall: 0.8750 - auc: 0.9672 - val_loss: 0.1882 - val_tp: 73.0000 - val_fp: 806.0000 - val_tn: 44680.0000 - val_fn: 10.0000 - val_accuracy: 0.9821 - val_precision: 0.0830 - val_recall: 0.8795 - val_auc: 0.9784\n",
      "Epoch 17/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2152 - tp: 271.0000 - fp: 6663.0000 - tn: 175309.0000 - fn: 33.0000 - accuracy: 0.9633 - precision: 0.0391 - recall: 0.8914 - auc: 0.9687 - val_loss: 0.1895 - val_tp: 73.0000 - val_fp: 754.0000 - val_tn: 44732.0000 - val_fn: 10.0000 - val_accuracy: 0.9832 - val_precision: 0.0883 - val_recall: 0.8795 - val_auc: 0.9785\n",
      "Epoch 18/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2420 - tp: 264.0000 - fp: 6535.0000 - tn: 175437.0000 - fn: 40.0000 - accuracy: 0.9639 - precision: 0.0388 - recall: 0.8684 - auc: 0.9610 - val_loss: 0.1895 - val_tp: 73.0000 - val_fp: 749.0000 - val_tn: 44737.0000 - val_fn: 10.0000 - val_accuracy: 0.9833 - val_precision: 0.0888 - val_recall: 0.8795 - val_auc: 0.9786\n",
      "Epoch 19/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2279 - tp: 268.0000 - fp: 6443.0000 - tn: 175529.0000 - fn: 36.0000 - accuracy: 0.9645 - precision: 0.0399 - recall: 0.8816 - auc: 0.9672 - val_loss: 0.1895 - val_tp: 73.0000 - val_fp: 763.0000 - val_tn: 44723.0000 - val_fn: 10.0000 - val_accuracy: 0.9830 - val_precision: 0.0873 - val_recall: 0.8795 - val_auc: 0.9788\n",
      "Epoch 20/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2247 - tp: 267.0000 - fp: 6596.0000 - tn: 175376.0000 - fn: 37.0000 - accuracy: 0.9636 - precision: 0.0389 - recall: 0.8783 - auc: 0.9684 - val_loss: 0.1896 - val_tp: 73.0000 - val_fp: 760.0000 - val_tn: 44726.0000 - val_fn: 10.0000 - val_accuracy: 0.9831 - val_precision: 0.0876 - val_recall: 0.8795 - val_auc: 0.9797\n",
      "Epoch 21/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2296 - tp: 269.0000 - fp: 6562.0000 - tn: 175410.0000 - fn: 35.0000 - accuracy: 0.9638 - precision: 0.0394 - recall: 0.8849 - auc: 0.9656 - val_loss: 0.1889 - val_tp: 73.0000 - val_fp: 750.0000 - val_tn: 44736.0000 - val_fn: 10.0000 - val_accuracy: 0.9833 - val_precision: 0.0887 - val_recall: 0.8795 - val_auc: 0.9797\n",
      "Epoch 22/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1982 - tp: 271.0000 - fp: 6583.0000 - tn: 175389.0000 - fn: 33.0000 - accuracy: 0.9637 - precision: 0.0395 - recall: 0.8914 - auc: 0.9756 - val_loss: 0.1879 - val_tp: 73.0000 - val_fp: 764.0000 - val_tn: 44722.0000 - val_fn: 10.0000 - val_accuracy: 0.9830 - val_precision: 0.0872 - val_recall: 0.8795 - val_auc: 0.9777\n",
      "Epoch 23/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2154 - tp: 273.0000 - fp: 6552.0000 - tn: 175420.0000 - fn: 31.0000 - accuracy: 0.9639 - precision: 0.0400 - recall: 0.8980 - auc: 0.9682 - val_loss: 0.1882 - val_tp: 73.0000 - val_fp: 762.0000 - val_tn: 44724.0000 - val_fn: 10.0000 - val_accuracy: 0.9831 - val_precision: 0.0874 - val_recall: 0.8795 - val_auc: 0.9779\n",
      "Epoch 24/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1861 - tp: 272.0000 - fp: 6248.0000 - tn: 175724.0000 - fn: 32.0000 - accuracy: 0.9655 - precision: 0.0417 - recall: 0.8947 - auc: 0.9779 - val_loss: 0.1885 - val_tp: 73.0000 - val_fp: 772.0000 - val_tn: 44714.0000 - val_fn: 10.0000 - val_accuracy: 0.9828 - val_precision: 0.0864 - val_recall: 0.8795 - val_auc: 0.9785\n",
      "Epoch 25/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1953 - tp: 270.0000 - fp: 6501.0000 - tn: 175471.0000 - fn: 34.0000 - accuracy: 0.9641 - precision: 0.0399 - recall: 0.8882 - auc: 0.9751 - val_loss: 0.1877 - val_tp: 73.0000 - val_fp: 768.0000 - val_tn: 44718.0000 - val_fn: 10.0000 - val_accuracy: 0.9829 - val_precision: 0.0868 - val_recall: 0.8795 - val_auc: 0.9786\n",
      "Epoch 26/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1704 - tp: 277.0000 - fp: 6215.0000 - tn: 175757.0000 - fn: 27.0000 - accuracy: 0.9658 - precision: 0.0427 - recall: 0.9112 - auc: 0.9808 - val_loss: 0.1903 - val_tp: 73.0000 - val_fp: 698.0000 - val_tn: 44788.0000 - val_fn: 10.0000 - val_accuracy: 0.9845 - val_precision: 0.0947 - val_recall: 0.8795 - val_auc: 0.9788\n",
      "Epoch 27/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1946 - tp: 271.0000 - fp: 6036.0000 - tn: 175936.0000 - fn: 33.0000 - accuracy: 0.9667 - precision: 0.0430 - recall: 0.8914 - auc: 0.9748 - val_loss: 0.1908 - val_tp: 73.0000 - val_fp: 692.0000 - val_tn: 44794.0000 - val_fn: 10.0000 - val_accuracy: 0.9846 - val_precision: 0.0954 - val_recall: 0.8795 - val_auc: 0.9786\n",
      "Epoch 28/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2115 - tp: 271.0000 - fp: 5873.0000 - tn: 176099.0000 - fn: 33.0000 - accuracy: 0.9676 - precision: 0.0441 - recall: 0.8914 - auc: 0.9688 - val_loss: 0.1914 - val_tp: 73.0000 - val_fp: 691.0000 - val_tn: 44795.0000 - val_fn: 10.0000 - val_accuracy: 0.9846 - val_precision: 0.0955 - val_recall: 0.8795 - val_auc: 0.9785\n",
      "Epoch 29/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2237 - tp: 266.0000 - fp: 6047.0000 - tn: 175925.0000 - fn: 38.0000 - accuracy: 0.9666 - precision: 0.0421 - recall: 0.8750 - auc: 0.9672 - val_loss: 0.1909 - val_tp: 73.0000 - val_fp: 698.0000 - val_tn: 44788.0000 - val_fn: 10.0000 - val_accuracy: 0.9845 - val_precision: 0.0947 - val_recall: 0.8795 - val_auc: 0.9784\n",
      "Epoch 30/100\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.2232 - tp: 272.0000 - fp: 5990.0000 - tn: 175982.0000 - fn: 32.0000 - accuracy: 0.9670 - precision: 0.0434 - recall: 0.8947 - auc: 0.9668 - val_loss: 0.1919 - val_tp: 73.0000 - val_fp: 642.0000 - val_tn: 44844.0000 - val_fn: 10.0000 - val_accuracy: 0.9857 - val_precision: 0.1021 - val_recall: 0.8795 - val_auc: 0.9785\n",
      "Epoch 31/100\n",
      "178176/182276 [============================>.] - ETA: 0s - loss: 0.2022 - tp: 273.0000 - fp: 5659.0000 - tn: 172216.0000 - fn: 28.0000 - accuracy: 0.9681 - precision: 0.0460 - recall: 0.9070 - auc: 0.9705Restoring model weights from the end of the best epoch.\n",
      "182276/182276 [==============================] - 1s 4us/sample - loss: 0.1989 - tp: 276.0000 - fp: 5796.0000 - tn: 176176.0000 - fn: 28.0000 - accuracy: 0.9680 - precision: 0.0455 - recall: 0.9079 - auc: 0.9708 - val_loss: 0.1920 - val_tp: 73.0000 - val_fp: 626.0000 - val_tn: 44860.0000 - val_fn: 10.0000 - val_accuracy: 0.9860 - val_precision: 0.1044 - val_recall: 0.8795 - val_auc: 0.9788\n",
      "Epoch 00031: early stopping\n"
     ]
    }
   ],
   "source": [
    "weighted_model = make_model()\n",
    "weighted_model.load_weights(initial_weights)\n",
    "\n",
    "weighted_history = weighted_model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks = [early_stopping],\n",
    "    validation_data=(val_features, val_labels),\n",
    "    # The class weights go here\n",
    "    class_weight=class_weight) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R0ynYRO0G3Lx"
   },
   "source": [
    "### Check training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BBe9FMO5ucTC"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(weighted_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "REy6WClTZIwQ"
   },
   "source": [
    "### Evaluate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nifqscPGw-5w"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "owKL2vdMBJr6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.06950428275801711\n",
      "tp :  94.0\n",
      "fp :  905.0\n",
      "tn :  55952.0\n",
      "fn :  11.0\n",
      "accuracy :  0.9839191\n",
      "precision :  0.0940941\n",
      "recall :  0.8952381\n",
      "auc :  0.9844724\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  55952\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  905\n",
      "Fraudulent Transactions Missed (False Negatives):  11\n",
      "Fraudulent Transactions Detected (True Positives):  94\n",
      "Total Fraudulent Transactions:  105\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_results = weighted_model.evaluate(test_features, test_labels,\n",
    "                                           batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(weighted_model.metrics_names, weighted_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_weighted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PTh1rtDn8r4-"
   },
   "source": [
    "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade offs between these different types of errors for your application."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hXDAwyr0HYdX"
   },
   "source": [
    "### Plot the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3hzScIVZS1Xm"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5f2017f7b8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "# Function legend() which is used to Place a legend on the axes\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5ysRtr6xHnXP"
   },
   "source": [
    "## Oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "18VUHNc-UF5w"
   },
   "source": [
    "### Oversample the minority class\n",
    "\n",
    "A related approach would be to resample the dataset by oversampling the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sHirNp6u7OWp"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "pos_features = train_features[bool_train_labels]\n",
    "neg_features = train_features[~bool_train_labels]\n",
    "\n",
    "pos_labels = train_labels[bool_train_labels]\n",
    "neg_labels = train_labels[~bool_train_labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WgBVbX7P7QrL"
   },
   "source": [
    "#### Using NumPy\n",
    "\n",
    "You can balance the dataset manually by choosing the right number of random \n",
    "indices from the positive examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BUzGjSkwqT88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181972, 29)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.arange() return evenly spaced values within a given interval.\n",
    "ids = np.arange(len(pos_features))\n",
    "# choice() method, you can get the random samples of one dimensional array and return the random samples of numpy array.\n",
    "choices = np.random.choice(ids, len(neg_features))\n",
    "\n",
    "res_pos_features = pos_features[choices]\n",
    "res_pos_labels = pos_labels[choices]\n",
    "\n",
    "res_pos_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7ie_FFet6cep"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363944, 29)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy.concatenate() function concatenate a sequence of arrays along an existing axis.\n",
    "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n",
    "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n",
    "\n",
    "order = np.arange(len(resampled_labels))\n",
    "# numpy.random.shuffle() modify a sequence in-place by shuffling its contents.\n",
    "np.random.shuffle(order)\n",
    "resampled_features = resampled_features[order]\n",
    "resampled_labels = resampled_labels[order]\n",
    "\n",
    "resampled_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IYfJe2Kc-FAz"
   },
   "source": [
    "#### Using `tf.data`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "usyixaST8v5P"
   },
   "source": [
    "If you're using `tf.data` the easiest way to produce balanced examples is to start with a `positive` and a `negative` dataset, and merge them. See [the tf.data guide](../../guide/data.ipynb) for more examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yF4OZ-rI6xb6"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 100000\n",
    "\n",
    "def make_ds(features, labels):\n",
    "# With the help of tf.data.Dataset.from_tensor_slices() method, we can get the slices of an array in the form of objects\n",
    "# by using tf.data.Dataset.from_tensor_slices() method.\n",
    "  ds = tf.data.Dataset.from_tensor_slices((features, labels))#.cache()\n",
    "  ds = ds.shuffle(BUFFER_SIZE).repeat()\n",
    "  return ds\n",
    "\n",
    "pos_ds = make_ds(pos_features, pos_labels)\n",
    "neg_ds = make_ds(neg_features, neg_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RNQUx-OA-oJc"
   },
   "source": [
    "Each dataset provides `(feature, label)` pairs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "llXc9rNH7Fbz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      " [-2.46955933  3.42534191 -4.42937043  3.70651659 -3.17895499 -1.30458304\n",
      " -5.          2.86676917 -4.9308611  -5.          3.58555137 -5.\n",
      "  1.51535494 -5.          0.01049775 -5.         -5.         -5.\n",
      "  2.02380731  0.36595419  1.61836304 -1.16743779  0.31324117 -0.35515978\n",
      " -0.62579636 -0.55952005  0.51255883  1.15454727  0.87478003]\n",
      "\n",
      "Label:  1\n"
     ]
    }
   ],
   "source": [
    "for features, label in pos_ds.take(1):\n",
    "  print(\"Features:\\n\", features.numpy())\n",
    "  print()\n",
    "  print(\"Label: \", label.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sLEfjZO0-vbN"
   },
   "source": [
    "Merge the two together using `experimental.sample_from_datasets`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e7w9UQPT9wzE"
   },
   "outputs": [],
   "source": [
    "# Samples elements at random from the datasets in `datasets`.\n",
    "resampled_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n",
    "resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EWXARdTdAuQK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48974609375\n"
     ]
    }
   ],
   "source": [
    "for features, label in resampled_ds.take(1):\n",
    "  print(label.numpy().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "irgqf3YxAyN0"
   },
   "source": [
    "To use this dataset, you'll need the number of steps per epoch.\n",
    "\n",
    "The definition of \"epoch\" in this case is less clear. Say it's the number of batches required to see each negative example once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xH-7K46AAxpq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# `np.ceil()` function returns the ceil value of the input array elements\n",
    "resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)\n",
    "resampled_steps_per_epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XZ1BvEpcBVHP"
   },
   "source": [
    "### Train on the oversampled data\n",
    "\n",
    "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n",
    "\n",
    "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "soRQ89JYqd6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 278.0 steps, validate for 23 steps\n",
      "Epoch 1/100\n",
      "278/278 [==============================] - 13s 48ms/step - loss: 0.4624 - tp: 267186.0000 - fp: 124224.0000 - tn: 160439.0000 - fn: 17495.0000 - accuracy: 0.7511 - precision: 0.6826 - recall: 0.9385 - auc: 0.9268 - val_loss: 0.3299 - val_tp: 79.0000 - val_fp: 2825.0000 - val_tn: 42661.0000 - val_fn: 4.0000 - val_accuracy: 0.9379 - val_precision: 0.0272 - val_recall: 0.9518 - val_auc: 0.9799\n",
      "Epoch 2/100\n",
      "278/278 [==============================] - 11s 39ms/step - loss: 0.2362 - tp: 264077.0000 - fp: 26654.0000 - tn: 257570.0000 - fn: 21043.0000 - accuracy: 0.9162 - precision: 0.9083 - recall: 0.9262 - auc: 0.9708 - val_loss: 0.1926 - val_tp: 75.0000 - val_fp: 1187.0000 - val_tn: 44299.0000 - val_fn: 8.0000 - val_accuracy: 0.9738 - val_precision: 0.0594 - val_recall: 0.9036 - val_auc: 0.9779\n",
      "Epoch 3/100\n",
      "278/278 [==============================] - 11s 40ms/step - loss: 0.1887 - tp: 263490.0000 - fp: 12935.0000 - tn: 271381.0000 - fn: 21538.0000 - accuracy: 0.9395 - precision: 0.9532 - recall: 0.9244 - auc: 0.9804 - val_loss: 0.1373 - val_tp: 75.0000 - val_fp: 1064.0000 - val_tn: 44422.0000 - val_fn: 8.0000 - val_accuracy: 0.9765 - val_precision: 0.0658 - val_recall: 0.9036 - val_auc: 0.9778\n",
      "Epoch 4/100\n",
      "278/278 [==============================] - 11s 41ms/step - loss: 0.1605 - tp: 263933.0000 - fp: 10513.0000 - tn: 274505.0000 - fn: 20393.0000 - accuracy: 0.9457 - precision: 0.9617 - recall: 0.9283 - auc: 0.9866 - val_loss: 0.1078 - val_tp: 75.0000 - val_fp: 1070.0000 - val_tn: 44416.0000 - val_fn: 8.0000 - val_accuracy: 0.9763 - val_precision: 0.0655 - val_recall: 0.9036 - val_auc: 0.9783\n",
      "Epoch 5/100\n",
      "278/278 [==============================] - 11s 39ms/step - loss: 0.1423 - tp: 265715.0000 - fp: 9592.0000 - tn: 275145.0000 - fn: 18892.0000 - accuracy: 0.9500 - precision: 0.9652 - recall: 0.9336 - auc: 0.9901 - val_loss: 0.0928 - val_tp: 75.0000 - val_fp: 1051.0000 - val_tn: 44435.0000 - val_fn: 8.0000 - val_accuracy: 0.9768 - val_precision: 0.0666 - val_recall: 0.9036 - val_auc: 0.9762\n",
      "Epoch 6/100\n",
      "278/278 [==============================] - 11s 40ms/step - loss: 0.1297 - tp: 267181.0000 - fp: 8944.0000 - tn: 275445.0000 - fn: 17774.0000 - accuracy: 0.9531 - precision: 0.9676 - recall: 0.9376 - auc: 0.9920 - val_loss: 0.0847 - val_tp: 75.0000 - val_fp: 1077.0000 - val_tn: 44409.0000 - val_fn: 8.0000 - val_accuracy: 0.9762 - val_precision: 0.0651 - val_recall: 0.9036 - val_auc: 0.9748\n",
      "Epoch 7/100\n",
      "278/278 [==============================] - 11s 39ms/step - loss: 0.1203 - tp: 267440.0000 - fp: 8606.0000 - tn: 276459.0000 - fn: 16839.0000 - accuracy: 0.9553 - precision: 0.9688 - recall: 0.9408 - auc: 0.9933 - val_loss: 0.0775 - val_tp: 75.0000 - val_fp: 1003.0000 - val_tn: 44483.0000 - val_fn: 8.0000 - val_accuracy: 0.9778 - val_precision: 0.0696 - val_recall: 0.9036 - val_auc: 0.9742\n",
      "Epoch 8/100\n",
      "278/278 [==============================] - 11s 40ms/step - loss: 0.1132 - tp: 268799.0000 - fp: 8165.0000 - tn: 276260.0000 - fn: 16120.0000 - accuracy: 0.9573 - precision: 0.9705 - recall: 0.9434 - auc: 0.9941 - val_loss: 0.0716 - val_tp: 75.0000 - val_fp: 927.0000 - val_tn: 44559.0000 - val_fn: 8.0000 - val_accuracy: 0.9795 - val_precision: 0.0749 - val_recall: 0.9036 - val_auc: 0.9713\n",
      "Epoch 9/100\n",
      "278/278 [==============================] - 11s 40ms/step - loss: 0.1074 - tp: 269627.0000 - fp: 7971.0000 - tn: 276559.0000 - fn: 15187.0000 - accuracy: 0.9593 - precision: 0.9713 - recall: 0.9467 - auc: 0.9947 - val_loss: 0.0670 - val_tp: 75.0000 - val_fp: 880.0000 - val_tn: 44606.0000 - val_fn: 8.0000 - val_accuracy: 0.9805 - val_precision: 0.0785 - val_recall: 0.9036 - val_auc: 0.9713\n",
      "Epoch 10/100\n",
      "278/278 [==============================] - 11s 39ms/step - loss: 0.1017 - tp: 270359.0000 - fp: 7590.0000 - tn: 277311.0000 - fn: 14084.0000 - accuracy: 0.9619 - precision: 0.9727 - recall: 0.9505 - auc: 0.9952 - val_loss: 0.0629 - val_tp: 75.0000 - val_fp: 848.0000 - val_tn: 44638.0000 - val_fn: 8.0000 - val_accuracy: 0.9812 - val_precision: 0.0813 - val_recall: 0.9036 - val_auc: 0.9717\n",
      "Epoch 11/100\n",
      "276/278 [============================>.] - ETA: 0s - loss: 0.0977 - tp: 269672.0000 - fp: 7408.0000 - tn: 274621.0000 - fn: 13547.0000 - accuracy: 0.9629 - precision: 0.9733 - recall: 0.9522 - auc: 0.9955Restoring model weights from the end of the best epoch.\n",
      "278/278 [==============================] - 11s 39ms/step - loss: 0.0978 - tp: 271609.0000 - fp: 7474.0000 - tn: 276625.0000 - fn: 13636.0000 - accuracy: 0.9629 - precision: 0.9732 - recall: 0.9522 - auc: 0.9955 - val_loss: 0.0615 - val_tp: 75.0000 - val_fp: 841.0000 - val_tn: 44645.0000 - val_fn: 8.0000 - val_accuracy: 0.9814 - val_precision: 0.0819 - val_recall: 0.9036 - val_auc: 0.9637\n",
      "Epoch 00011: early stopping\n"
     ]
    }
   ],
   "source": [
    "resampled_model = make_model()\n",
    "resampled_model.load_weights(initial_weights)\n",
    "\n",
    "# Reset the bias to zero, since this dataset is balanced.\n",
    "output_layer = resampled_model.layers[-1] \n",
    "output_layer.bias.assign([0])\n",
    "\n",
    "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n",
    "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2) \n",
    "\n",
    "resampled_history = resampled_model.fit(\n",
    "    resampled_ds,\n",
    "    epochs=EPOCHS,\n",
    "    steps_per_epoch=resampled_steps_per_epoch,\n",
    "    callbacks = [early_stopping],\n",
    "    validation_data=val_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "avALvzUp3T_c"
   },
   "source": [
    "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n",
    "\n",
    "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n",
    "\n",
    "This smoother gradient signal makes it easier to train the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "klHZ0HV76VC5"
   },
   "source": [
    "### Check training history\n",
    "\n",
    "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YoUGfr1vuivl"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(resampled_history )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1PuH3A2vnwrh"
   },
   "source": [
    "### Re-train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KFLxRL8eoDE5"
   },
   "source": [
    "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n",
    "\n",
    "So break up the epochs to give the `callbacks.EarlyStopping` finer control over when to stop training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e_yn9I26qAHU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 20 steps, validate for 23 steps\n",
      "Epoch 1/1000\n",
      "20/20 [==============================] - 4s 181ms/step - loss: 0.8800 - tp: 18783.0000 - fp: 16378.0000 - tn: 4036.0000 - fn: 1763.0000 - accuracy: 0.5571 - precision: 0.5342 - recall: 0.9142 - auc: 0.7752 - val_loss: 1.3661 - val_tp: 83.0000 - val_fp: 40065.0000 - val_tn: 5421.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.1208 - val_precision: 0.0021 - val_recall: 1.0000 - val_auc: 0.9425\n",
      "Epoch 2/1000\n",
      "20/20 [==============================] - 1s 35ms/step - loss: 0.7378 - tp: 19613.0000 - fp: 15282.0000 - tn: 5187.0000 - fn: 878.0000 - accuracy: 0.6055 - precision: 0.5621 - recall: 0.9572 - auc: 0.8680 - val_loss: 1.1629 - val_tp: 83.0000 - val_fp: 36851.0000 - val_tn: 8635.0000 - val_fn: 0.0000e+00 - val_accuracy: 0.1913 - val_precision: 0.0022 - val_recall: 1.0000 - val_auc: 0.9580\n",
      "Epoch 3/1000\n",
      "20/20 [==============================] - 1s 39ms/step - loss: 0.6431 - tp: 19522.0000 - fp: 13990.0000 - tn: 6558.0000 - fn: 890.0000 - accuracy: 0.6367 - precision: 0.5825 - recall: 0.9564 - auc: 0.8950 - val_loss: 0.9853 - val_tp: 82.0000 - val_fp: 32268.0000 - val_tn: 13218.0000 - val_fn: 1.0000 - val_accuracy: 0.2919 - val_precision: 0.0025 - val_recall: 0.9880 - val_auc: 0.9660\n",
      "Epoch 4/1000\n",
      "20/20 [==============================] - 1s 39ms/step - loss: 0.5563 - tp: 19488.0000 - fp: 12475.0000 - tn: 8032.0000 - fn: 965.0000 - accuracy: 0.6719 - precision: 0.6097 - recall: 0.9528 - auc: 0.9135 - val_loss: 0.8430 - val_tp: 82.0000 - val_fp: 26633.0000 - val_tn: 18853.0000 - val_fn: 1.0000 - val_accuracy: 0.4155 - val_precision: 0.0031 - val_recall: 0.9880 - val_auc: 0.9713\n",
      "Epoch 5/1000\n",
      "20/20 [==============================] - 1s 37ms/step - loss: 0.4984 - tp: 19489.0000 - fp: 11049.0000 - tn: 9377.0000 - fn: 1045.0000 - accuracy: 0.7047 - precision: 0.6382 - recall: 0.9491 - auc: 0.9242 - val_loss: 0.7307 - val_tp: 82.0000 - val_fp: 20850.0000 - val_tn: 24636.0000 - val_fn: 1.0000 - val_accuracy: 0.5424 - val_precision: 0.0039 - val_recall: 0.9880 - val_auc: 0.9753\n",
      "Epoch 6/1000\n",
      "20/20 [==============================] - 1s 39ms/step - loss: 0.4463 - tp: 19305.0000 - fp: 9622.0000 - tn: 10895.0000 - fn: 1138.0000 - accuracy: 0.7373 - precision: 0.6674 - recall: 0.9443 - auc: 0.9336 - val_loss: 0.6405 - val_tp: 82.0000 - val_fp: 15843.0000 - val_tn: 29643.0000 - val_fn: 1.0000 - val_accuracy: 0.6523 - val_precision: 0.0051 - val_recall: 0.9880 - val_auc: 0.9773\n",
      "Epoch 7/1000\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.4121 - tp: 19365.0000 - fp: 8524.0000 - tn: 11931.0000 - fn: 1140.0000 - accuracy: 0.7641 - precision: 0.6944 - recall: 0.9444 - auc: 0.9411 - val_loss: 0.5691 - val_tp: 82.0000 - val_fp: 11981.0000 - val_tn: 33505.0000 - val_fn: 1.0000 - val_accuracy: 0.7371 - val_precision: 0.0068 - val_recall: 0.9880 - val_auc: 0.9787\n",
      "Epoch 8/1000\n",
      "20/20 [==============================] - 1s 39ms/step - loss: 0.3784 - tp: 19242.0000 - fp: 7375.0000 - tn: 13072.0000 - fn: 1271.0000 - accuracy: 0.7889 - precision: 0.7229 - recall: 0.9380 - auc: 0.9461 - val_loss: 0.5120 - val_tp: 80.0000 - val_fp: 9309.0000 - val_tn: 36177.0000 - val_fn: 3.0000 - val_accuracy: 0.7957 - val_precision: 0.0085 - val_recall: 0.9639 - val_auc: 0.9794\n",
      "Epoch 9/1000\n",
      "20/20 [==============================] - 1s 45ms/step - loss: 0.3551 - tp: 19106.0000 - fp: 6529.0000 - tn: 13989.0000 - fn: 1336.0000 - accuracy: 0.8080 - precision: 0.7453 - recall: 0.9346 - auc: 0.9495 - val_loss: 0.4657 - val_tp: 80.0000 - val_fp: 7354.0000 - val_tn: 38132.0000 - val_fn: 3.0000 - val_accuracy: 0.8386 - val_precision: 0.0108 - val_recall: 0.9639 - val_auc: 0.9799\n",
      "Epoch 10/1000\n",
      "20/20 [==============================] - 1s 38ms/step - loss: 0.3350 - tp: 19149.0000 - fp: 5794.0000 - tn: 14698.0000 - fn: 1319.0000 - accuracy: 0.8263 - precision: 0.7677 - recall: 0.9356 - auc: 0.9535 - val_loss: 0.4275 - val_tp: 80.0000 - val_fp: 5832.0000 - val_tn: 39654.0000 - val_fn: 3.0000 - val_accuracy: 0.8720 - val_precision: 0.0135 - val_recall: 0.9639 - val_auc: 0.9802\n",
      "Epoch 11/1000\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.3168 - tp: 19224.0000 - fp: 5013.0000 - tn: 15322.0000 - fn: 1401.0000 - accuracy: 0.8434 - precision: 0.7932 - recall: 0.9321 - auc: 0.9552 - val_loss: 0.3969 - val_tp: 80.0000 - val_fp: 4730.0000 - val_tn: 40756.0000 - val_fn: 3.0000 - val_accuracy: 0.8961 - val_precision: 0.0166 - val_recall: 0.9639 - val_auc: 0.9805\n",
      "Epoch 12/1000\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.3077 - tp: 19028.0000 - fp: 4564.0000 - tn: 16058.0000 - fn: 1310.0000 - accuracy: 0.8566 - precision: 0.8065 - recall: 0.9356 - auc: 0.9593 - val_loss: 0.3695 - val_tp: 80.0000 - val_fp: 3819.0000 - val_tn: 41667.0000 - val_fn: 3.0000 - val_accuracy: 0.9161 - val_precision: 0.0205 - val_recall: 0.9639 - val_auc: 0.9804\n",
      "Epoch 13/1000\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.2936 - tp: 19047.0000 - fp: 4028.0000 - tn: 16444.0000 - fn: 1441.0000 - accuracy: 0.8665 - precision: 0.8254 - recall: 0.9297 - auc: 0.9597 - val_loss: 0.3461 - val_tp: 79.0000 - val_fp: 3149.0000 - val_tn: 42337.0000 - val_fn: 4.0000 - val_accuracy: 0.9308 - val_precision: 0.0245 - val_recall: 0.9518 - val_auc: 0.9802\n",
      "Epoch 14/1000\n",
      "20/20 [==============================] - 1s 38ms/step - loss: 0.2829 - tp: 19087.0000 - fp: 3596.0000 - tn: 16855.0000 - fn: 1422.0000 - accuracy: 0.8775 - precision: 0.8415 - recall: 0.9307 - auc: 0.9619 - val_loss: 0.3266 - val_tp: 79.0000 - val_fp: 2691.0000 - val_tn: 42795.0000 - val_fn: 4.0000 - val_accuracy: 0.9409 - val_precision: 0.0285 - val_recall: 0.9518 - val_auc: 0.9803\n",
      "Epoch 15/1000\n",
      "20/20 [==============================] - 1s 39ms/step - loss: 0.2748 - tp: 19020.0000 - fp: 3174.0000 - tn: 17283.0000 - fn: 1483.0000 - accuracy: 0.8863 - precision: 0.8570 - recall: 0.9277 - auc: 0.9627 - val_loss: 0.3095 - val_tp: 79.0000 - val_fp: 2360.0000 - val_tn: 43126.0000 - val_fn: 4.0000 - val_accuracy: 0.9481 - val_precision: 0.0324 - val_recall: 0.9518 - val_auc: 0.9797\n",
      "Epoch 16/1000\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.2666 - tp: 18890.0000 - fp: 2889.0000 - tn: 17757.0000 - fn: 1424.0000 - accuracy: 0.8947 - precision: 0.8673 - recall: 0.9299 - auc: 0.9653 - val_loss: 0.2945 - val_tp: 78.0000 - val_fp: 2101.0000 - val_tn: 43385.0000 - val_fn: 5.0000 - val_accuracy: 0.9538 - val_precision: 0.0358 - val_recall: 0.9398 - val_auc: 0.9796\n",
      "Epoch 17/1000\n",
      "20/20 [==============================] - 1s 38ms/step - loss: 0.2583 - tp: 18959.0000 - fp: 2517.0000 - tn: 17973.0000 - fn: 1511.0000 - accuracy: 0.9017 - precision: 0.8828 - recall: 0.9262 - auc: 0.9657 - val_loss: 0.2817 - val_tp: 78.0000 - val_fp: 1929.0000 - val_tn: 43557.0000 - val_fn: 5.0000 - val_accuracy: 0.9576 - val_precision: 0.0389 - val_recall: 0.9398 - val_auc: 0.9794\n",
      "Epoch 18/1000\n",
      "20/20 [==============================] - 1s 46ms/step - loss: 0.2511 - tp: 19104.0000 - fp: 2344.0000 - tn: 18043.0000 - fn: 1469.0000 - accuracy: 0.9069 - precision: 0.8907 - recall: 0.9286 - auc: 0.9678 - val_loss: 0.2704 - val_tp: 78.0000 - val_fp: 1787.0000 - val_tn: 43699.0000 - val_fn: 5.0000 - val_accuracy: 0.9607 - val_precision: 0.0418 - val_recall: 0.9398 - val_auc: 0.9793\n",
      "Epoch 19/1000\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.2445 - tp: 19183.0000 - fp: 2087.0000 - tn: 18215.0000 - fn: 1475.0000 - accuracy: 0.9130 - precision: 0.9019 - recall: 0.9286 - auc: 0.9693 - val_loss: 0.2598 - val_tp: 78.0000 - val_fp: 1665.0000 - val_tn: 43821.0000 - val_fn: 5.0000 - val_accuracy: 0.9634 - val_precision: 0.0448 - val_recall: 0.9398 - val_auc: 0.9791\n",
      "Epoch 20/1000\n",
      "20/20 [==============================] - 1s 39ms/step - loss: 0.2373 - tp: 18995.0000 - fp: 1906.0000 - tn: 18602.0000 - fn: 1457.0000 - accuracy: 0.9179 - precision: 0.9088 - recall: 0.9288 - auc: 0.9712 - val_loss: 0.2500 - val_tp: 78.0000 - val_fp: 1587.0000 - val_tn: 43899.0000 - val_fn: 5.0000 - val_accuracy: 0.9651 - val_precision: 0.0468 - val_recall: 0.9398 - val_auc: 0.9788\n",
      "Epoch 21/1000\n",
      "19/20 [===========================>..] - ETA: 0s - loss: 0.2378 - tp: 18121.0000 - fp: 1821.0000 - tn: 17599.0000 - fn: 1371.0000 - accuracy: 0.9180 - precision: 0.9087 - recall: 0.9297 - auc: 0.9714Restoring model weights from the end of the best epoch.\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.2376 - tp: 19083.0000 - fp: 1918.0000 - tn: 18513.0000 - fn: 1446.0000 - accuracy: 0.9179 - precision: 0.9087 - recall: 0.9296 - auc: 0.9714 - val_loss: 0.2401 - val_tp: 78.0000 - val_fp: 1485.0000 - val_tn: 44001.0000 - val_fn: 5.0000 - val_accuracy: 0.9673 - val_precision: 0.0499 - val_recall: 0.9398 - val_auc: 0.9785\n",
      "Epoch 00021: early stopping\n"
     ]
    }
   ],
   "source": [
    "resampled_model = make_model()\n",
    "resampled_model.load_weights(initial_weights)\n",
    "\n",
    "# Reset the bias to zero, since this dataset is balanced.\n",
    "output_layer = resampled_model.layers[-1] \n",
    "output_layer.bias.assign([0])\n",
    "\n",
    "resampled_history = resampled_model.fit(\n",
    "    resampled_ds,\n",
    "    # These are not real epochs\n",
    "    steps_per_epoch = 20,\n",
    "    epochs=10*EPOCHS,\n",
    "    callbacks = [early_stopping],\n",
    "    validation_data=(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UuJYKv0gpBK1"
   },
   "source": [
    "### Re-check training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FMycrpJwn39w"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(resampled_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bUuE5HOWZiwP"
   },
   "source": [
    "### Evaluate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C0fmHSgXxFdW"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FO0mMOYUDWFk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.3960801533448772\n",
      "tp :  99.0\n",
      "fp :  5892.0\n",
      "tn :  50965.0\n",
      "fn :  6.0\n",
      "accuracy :  0.8964573\n",
      "precision :  0.016524788\n",
      "recall :  0.94285715\n",
      "auc :  0.9804354\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  50965\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  5892\n",
      "Fraudulent Transactions Missed (False Negatives):  6\n",
      "Fraudulent Transactions Detected (True Positives):  99\n",
      "Total Fraudulent Transactions:  105\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampled_results = resampled_model.evaluate(test_features, test_labels,\n",
    "                                             batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(resampled_model.metrics_names, resampled_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_xYozM1IIITq"
   },
   "source": [
    "### Plot the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fye_CiuYrZ1U"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5eebd220b8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Resampled\", train_labels, train_predictions_resampled,  color=colors[2])\n",
    "plot_roc(\"Test Resampled\", test_labels, test_predictions_resampled,  color=colors[2], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3o3f0ywl8uqW"
   },
   "source": [
    "## Applying this tutorial to your problem\n",
    "\n",
    "Imbalanced data classification is an inherantly difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "imbalanced_data.ipynb",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
